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String Similarity Search/Join
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Abstract
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We present in this paper scalable algorithms for optimal string similarity
search and join. Our methods are variations of those applied in Masai, our
recently published tool for mapping high-throughput DNA sequencing data with
unpreceded speed and accuracy. The key features of our approach are filtration
with approximate seeds and methods for multiple backtracking. Approximate
seeds, compared to exact seeds, increase filtration specificity while
preserving sensitivity. Multiple backtracking amortizes the cost of searching
a large set of seeds. Combined together, these two methods significantly speed
up string similarity search and join operations.
For more information see
https://www.seqan.de/apps/edbt2013/
References
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* Siragusa, E., Weese D., & Reinert, K. (2013). Scalable String Similarity
Search/Join with Approximate Seeds and Multiple Backtracking.
EDBT/ICDT ’13, March 18--22 2013, Genoa, Italy
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